You're reading from Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781800567689

Length 312 pages

Edition 1st Edition

Languages

Python

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Adnan Masood

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: Introduction to Automated Machine Learning

2. Chapter 1: A Lap around Automated Machine Learning FREE CHAPTER

3. Chapter 2: Automated Machine Learning, Algorithms, and Techniques

4. Chapter 3: Automated Machine Learning with Open Source Tools and Libraries

5. Section 2: AutoML with Cloud Platforms

6. Chapter 4: Getting Started with Azure Machine Learning

7. Chapter 5: Automated Machine Learning with Microsoft Azure

8. Chapter 6: Machine Learning with AWS

9. Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot

10. Chapter 8: Machine Learning with Google Cloud Platform

11. Chapter 9: Automated Machine Learning with GCP

12. Section 3: Applied Automated Machine Learning

13. Chapter 10: AutoML in the Enterprise

14. Other Books You May Enjoy

The Azure Machine Learning stack

The Microsoft Azure ecosystem is quite broad; in this chapter, we will focus on its AI and ML related cloud offerings, especially the Azure Machine Learning service.

The following figure shows the offerings available for ML in the Azure cloud:

Figure 4.2 – Azure cloud ML offerings

You can visit the following link for more information about the offerings in the preceding table:

It can be confusing to know which Azure Machine Learning offering should be chosen among the many described in the preceding table. The following diagram helps with choosing the right offering based on the given business and technology scenario:

Figure 4.3 – Azure Machine Learning decision flow

Automated ML is a part of the Azure Machine Learning service capabilities. Other capabilities include collaborative notebooks, data labeling, ML operations, a drag-and-drop designer studio, autoscaling capabilities...